摘要
面向分布式与集中式相结合的分层无人集群系统,在不同的场景下面临着各方面的多重挑战。利用无人集群设备进行高效的环境态势协同感知,其核心在于如何将各种类型的模态感知数据进行高效率的压缩及映射,从而实现高效且可靠的传输。文章旨在利用无监督哈希学习算法提高无人集群系统中数据处理效率、协同感知精度以及资源利用效率,从而为无人集群协同感知技术提供有效方案。
The layered unmanned cluster system that combines distributed and centralized approaches faces multiple challenges in different scenarios.The core of utilizing unmanned cluster devices for efficient environmental situational awareness lies in how to efficiently compress and map various types of modal perception data,thereby achieving efficient and reliable transmission.This article aims to use unsupervised hash learning algorithms to improve data processing efficiency,collaborative perception accuracy,and resource utilization efficiency in unmanned cluster systems,providing effective solutions for collaborative perception technology in unmanned clusters.
作者
耿恺频
GENG Kaipin(Nanjing Yaxingwei Information Technology Co.,Ltd.,Nanjing 210016,China)
出处
《无线互联科技》
2025年第14期18-22,38,共6页
Wireless Internet Science and Technology
基金
国家重点研发计划项目,项目编号:2018YFB1403400。江苏省市场监督管理局科技计划项目,项目编号:KJ21125108,KJ207565。
关键词
无人集群
哈希学习
协同感知
unmanned cluster
hash learning
collaborative perception
